L

LLMs

Jarvis AI

🤖 AI chat & search summaries in Google Search, powered by the latest LLMs

cli llm chat client written in nim with support of ollama and openai

A unified Python interface to select and use multiple Large Language Model (LLM) providers through a common API.

💬 Epic prompts to turbo-charge your LLM chatbots.

🔎 Type @you in browser address bar to get results from You.com AI

🔎 Type @brave in browser address bar to get results from Brave AI

🛒 AI chat & product/category summaries in Amazon shopping, powered by the latest LLMs

Rust implementation of the Agent2Agent (A2A) Protocol (https://a2a-protocol.org/), an open standard designed to enable seamless communication and collaboration between AI agents.

Convert OpenAPI and Postman to Wrekenfile and break down to mini-wrekenfiles for LLM context

KiM Explorer is a two-stage RAG application for transport policy research publications from the KiM Netherlands Institute for Transport Policy Analysis. Users perform semantic search to identify relevant documents, manually select publications, then interact with an LLM using full document context rather than chunks. Built with Python/NiceGUI/OpenAI API, featuring citation generation, conversation history, filtering, and web/CLI interfaces. https://explorer.kim.rijkscloud.nl/

AI Image Renamer is a Python CLI tool that leverages generative artificial intelligence to rename your images based on what the image shows.

Project for collecting requirements from feature teams interested in semantic search and RAG-powered features.

Buzz is a small, self-contained companion project designed to feel calm, present, and kind.

🤖 nGPT: A Swiss army knife for LLMs: powerful CLI and interactive chatbot in one package. Seamlessly work with OpenAI, Ollama, Groq, Claude, Gemini, or any OpenAI-compatible API to generate code, craft git commits, rewrite text, and execute shell commands. Fast, lightweight, and designed for both casual users and developers.

https://nazdridoy.github.io/ngpt/

C++ LLM Client Using OpenRouter API

This project demonstrates how to integrate Large Language Models (LLMs) into native C++ applications using the OpenRouter API.

Key Features

OpenRouter API Integration Connect to a wide range of AI models via OpenRouter's unified API endpoint.

C++ Implementation Written in modern C++ for portability and efficiency.

Command-Line Interface Simple text-based interface for interacting with AI models.

Easy Configuration Set your API key and preferred model in a config.json file: api_key, url, model. Example:

{ "api_key": "", "url": "https://openrouter.ai/api/v1/chat/completions", "model": "deepseek/deepseek-r1-0528-qwen3-8b:free" }

Dependencies

C++11-compliant and forward-compatible

libcurl (for HTTP requests)

nlohmann/json (for JSON parsing)

Educational Value

Learn how to integrate third-party APIs in C++

Use C++ to build a minimal conversational AI interface

Serve as a starting point for more advanced native AI applications

distributed cognitive framework in Elixir/OTP.

It combines Nabla-Infinity recursive introspection (∇∞), blackboard-based shared memory, and multi-agent systems with LLM integration.

Designed for research, reasoning, training simulations, and advanced AI applications.